BER vs SNR from Channel Impulse Response in MATLAB

In this MATLAB code it is described how to calculate 'BER vs SNR' and 'SER vs SNR' from the Channel Impulse Response (CIR). 

SER =  Symbol error rate

For a typical communication system, if we transmit the signal x[n], then received signal will be,

y[n] = h[n]*x[n]  + noise

Where, h[n] is channel impulse response and '*' indicates convolution operation

MATLAB Code

clc;
clear all;
close all;

% Channel Impulse Response (CIR)
channel_response = [0.85]; % Example channel impulse response coefficients
%channel_response = [0.8, 0.1];
% Modulation parameters
modulation_order = 4; % QPSK modulation
num_symbols = 1000; % Number of symbols to transmit
num_bits = 1000*log2(modulation_order);

% Signal generation
tx_symbols = randi([0, modulation_order-1], 1, num_symbols);
tx_signal = qammod(tx_symbols, modulation_order);

% Channel simulation
rx_signal = conv(tx_signal, channel_response);

% Add AWGN (Additive White Gaussian Noise)
snr_dB = 0:2:20;
ber = zeros(size(snr_dB));

for i = 1:length(snr_dB)
noise_power = 10^(-snr_dB(i)/10);
rx_noisy_signal = rx_signal + sqrt(noise_power) * randn(size(rx_signal));

% Receiver processing
rx_symbols = qamdemod(rx_noisy_signal, modulation_order);

% BER calculation
a = reshape(de2bi(rx_symbols(1:num_symbols)),[1,num_bits]);
b = reshape(de2bi(tx_symbols),[1,num_bits]);

ber(i) = sum(a ~= b) / num_bits;
ser(i) = sum(rx_symbols(1:1000) ~= tx_symbols) / num_symbols;
end

% Plot BER vs. SNR curve
figure(1);
semilogy(snr_dB, ber, 'o-');
xlabel('SNR (dB)');
ylabel('Bit Error Rate (BER)');
title('BER vs. SNR');

grid on;

% Plot BER vs. SNR curve
figure(2);
semilogy(snr_dB, ser, 'o-');
xlabel('SNR (dB)');
ylabel('Symbol Error Rate (BER)');
title('SER vs. SNR');

grid on;

Output

 

 

 

 

 

 

 

 

 

 

Copy the aforementioned MATLAB Code from here 

clc; clear all; close all; % Channel Impulse Response (CIR) channel_response = [0.85]; % Example channel impulse response coefficients %channel_response = [0.8, 0.1]; % Modulation parameters modulation_order = 4; % QPSK modulation num_symbols = 1000; % Number of symbols to transmit num_bits = 1000*log2(modulation_order); % Signal generation tx_symbols = randi([0, modulation_order-1], 1, num_symbols); tx_signal = qammod(tx_symbols, modulation_order); % Channel simulation rx_signal = conv(tx_signal, channel_response); % Add AWGN (Additive White Gaussian Noise) snr_dB = 0:2:20; ber = zeros(size(snr_dB)); for i = 1:length(snr_dB) noise_power = 10^(-snr_dB(i)/10); rx_noisy_signal = rx_signal + sqrt(noise_power) * randn(size(rx_signal)); % Receiver processing rx_symbols = qamdemod(rx_noisy_signal, modulation_order); % BER calculation a = reshape(de2bi(rx_symbols(1:num_symbols)),[1,num_bits]); b = reshape(de2bi(tx_symbols),[1,num_bits]); ber(i) = sum(a ~= b) / num_bits; ser(i) = sum(rx_symbols(1:1000) ~= tx_symbols) / num_symbols; end % Plot BER vs. SNR curve figure(1); semilogy(snr_dB, ber, 'o-'); xlabel('SNR (dB)'); ylabel('Bit Error Rate (BER)'); title('BER vs. SNR'); grid on; % Plot BER vs. SNR curve figure(2); semilogy(snr_dB, ser, 'o-'); xlabel('SNR (dB)'); ylabel('Symbol Error Rate (BER)'); title('SER vs. SNR'); grid on;